DATDAT-003 — Data Bias and Fairness Oversights
AI Decision Systems Reproduce Bias Through Biased Criteria and Historical Data
4/5Sector: OtherGeography: GlobalStage: OperateIngested: —
Executive Summary
AI systems generate discriminatory outcomes when trained on historically biased data or built around criteria that embed structural inequality. Boards face legal exposure and reputational harm if algorithmic decisions affecting people are not subject to regular bias audits and human oversight.
Domain
Data Management
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Source
MIT AI Risk Repository — Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study (Paes2023) ↗https://airisk.mit.edu/
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